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Calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.
The arithmetic mean is defined as
npm install @stdlib/stats-base-smeankbn2
var smeankbn2 = require( '@stdlib/stats-base-smeankbn2' );
Computes the arithmetic mean of a single-precision floating-point strided array x
using a second-order iterative Kahan–Babuška algorithm.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = smeankbn2( N, x, 1 );
// returns ~0.3333
The function has the following parameters:
- N: number of indexed elements.
-
x: input
Float32Array
. -
stride: index increment for
x
.
The N
and stride
parameters determine which elements in x
are accessed at runtime. For example, to compute the arithmetic mean of every other element in x
,
var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var N = floor( x.length / 2 );
var v = smeankbn2( N, x, 2 );
// returns 1.25
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = smeankbn2( N, x1, 2 );
// returns 1.25
Computes the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = smeankbn2.ndarray( N, x, 1, 0 );
// returns ~0.33333
The function has the following additional parameters:
-
offset: starting index for
x
.
While typed array
views mandate a view offset based on the underlying buffer
, the offset
parameter supports indexing semantics based on a starting index. For example, to calculate the arithmetic mean for every other value in x
starting from the second value
var Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var N = floor( x.length / 2 );
var v = smeankbn2.ndarray( N, x, 2, 1 );
// returns 1.25
- If
N <= 0
, both functions returnNaN
.
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var smeankbn2 = require( '@stdlib/stats-base-smeankbn2' );
var x;
var i;
x = new Float32Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( (randu()*100.0) - 50.0 );
}
console.log( x );
var v = smeankbn2( x.length, x, 1 );
console.log( v );
- Klein, Andreas. 2005. "A Generalized Kahan-Babuška-Summation-Algorithm." Computing 76 (3): 279–93. doi:10.1007/s00607-005-0139-x.
-
@stdlib/stats-base/dmeankbn2
: calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. -
@stdlib/stats-base/meankbn2
: calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm. -
@stdlib/stats-base/smean
: calculate the arithmetic mean of a single-precision floating-point strided array.
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
See LICENSE.
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